Introduction
Urban energy management in Zambia is experiencing both a pressing demand for energy infrastructure expansion and a potential for innovative, data-driven solutions. As it stands, only 43% of the Zambian population is connected to the national power grid, with an urban–rural divide of 67% and 14.5%. Over-reliance on hydropower, which accounts for about 80% of the country's electricity, represents a critical vulnerability to climate change. A severe drought in the 2023/24 period led to an 80% reduction in electricity production from Lake Kariba, leaving Lusaka residents with up to 20 hours of daily power outages. This has led to a need for smarter energy management systems that can optimise limited resources, anticipate energy needs, and incorporate distributed renewable energy sources. The National Digital Transformation Strategy 2023–2027 and Vision 2030 framework have laid the foundation for AI adoption, digital twin technologies, and data-driven decision support systems. The Ministry of Technology and Science has stated that 'digital transformation is as much about technology as it is about people, equality and the just distribution of resources and sustainable development.' This paper will investigate the feasibility, implementation considerations, and contextual adaptation required for the use of AI to support urban energy management systems in Zambia, focusing on aspects such as solar photovoltaic (PV) integration, demand-side management, and smart grid frameworks.

Methods
The study was conducted through a literature review, document review, and case study analysis. The literature review focused on AI in smart energy systems, digital twins for infrastructure management, and smart city projects in Sub-Saharan Africa. The document review focused on Zambia's National Energy Policy, National Digital Transformation Strategy, Integrated Resource Plan, and related regulations and policies for electricity generation and distribution. Case study analysis covered smart energy projects in Africa, including Kenya's renewable energy diversification, Rwanda's smart grid pilots, and Zambia's Smart Village project. Findings were synthesised and presented under four thematic areas: technological readiness and infrastructure; institutional and regulatory frameworks; human capacity and skills; and financing and investment. Attention was given to digital twin modelling for Zambia's electricity distribution grid and to the evaluation of machine learning algorithms for solar irradiance prediction, given Zambia's high solar resource.

Results
There are strong opportunities for AI solutions in the Zambian energy management space, but multiple challenges remain for project implementation. Machine learning models for load forecasting, particularly for shorter horizons, have established technical viability; this capacity to model and predict load behaviour on the grid enables more efficient management of generation units. Digital twin technology has strong applications for network simulation in Zambian cities, particularly Lusaka, enabling scenario modelling such as fault analysis, technical loss quantification, and network reconfiguration without physical intervention. Solar irradiance prediction tools for distributed solar photovoltaic (PV) integration are highly beneficial to Zambia's national target of 1,000 megawatt (MW) solar PV capacity by 2025 and the related task of addressing solar PV generation intermittency. Local challenges that persist in the context of AI include low internet penetration in peri-urban environments, limiting the viability of IoT-connected sensor nodes; skill and experience gaps in local human capital, resulting in a bottleneck for system upkeep and tuning of machine learning models; and a lack of financial capital to deploy these use cases, such as smart meter rollouts. National fibre coverage for smart city projects across Lusaka and other major cities, such as those aligned with DTRA's smart poles, which include micro base stations and 4G/5G connectivity, provides a technological foundation for scaling up but is not yet broad enough to be deployed outside business districts. The UNDP initiative, Timbuktoo AI Compute Nodes, aims to provide nations in Africa, including Zambia, with their own sovereign AI computer cloud powered by renewable energy and could set precedents for regional and local AI capacity-building that integrate environmental and sustainability goals. Policy development for data privacy and security is needed, as well as regulation for decentralised models such as peer-to-peer energy trading.

Conclusions
Incorporating AI, digital twins, and data-driven decision support into ZESCO's operations is a forward-thinking approach that can potentially revolutionise urban energy management in Zambia. These technologies promise to optimise energy distribution and consumption, even within the constraints of existing infrastructure, while also facilitating large-scale integration of renewable energy. This can be achieved through a combination of strategic actions, including the deployment of smart grid technologies, investment in human capital, modernisation of regulatory frameworks, and mobilisation of innovative financing mechanisms. Immediate priorities could include establishing pilot digital twin projects for specific urban distribution networks, developing machine learning models tailored to Zambian load and generation data, and building technical capacity within ZESCO and municipal authorities. Leveraging international partnerships and knowledge transfer can also expedite progress while ensuring contextual relevance. As Zambia advances towards its Vision 2030 development goals, intelligent energy systems are not just technological enhancements but fundamental drivers of inclusive and sustainable urban growth.